Denoising diffusion probabilistic models (DDPMs) are becoming the leading paradigm for generative models. It has recently shown breakthroughs in audio synthesis and time series imputation. However, little attention has been given to leveraging the powerful generative ability for general time series production. In this paper, we propose Diffusion-TS, the first DDPM-based framework that generates multivariate time series samples of high quality by using an encoder-decoder Transformer with disentangled temporal representations, in which the decomposition technique guides Diffusion-TS to capture the semantic meaning of time series while Transformers mine detailed sequential information from the noisy model input. For more interpretable and accurate pattern modeling, we train the model to directly reconstruct the sample instead of the noise in each diffusion step, combining a Fourier-based loss term. In addition, it is shown that the proposed Diffusion-TS can be easily extended to conditional generation tasks, such as forecasting and imputation, without any model changes. This also motivates us to further explore the performance of Diffusion-TS under irregular settings. Finally, through qualitative and quantitative experiments, results show that Diffusion-TS achieves the state-of-the-art results on various realistic analyses of time series. Our code and models are attached in the supplementary material and will be made publicly available.